def resolve_fits_files(infile):
'''This function returns a properly formatted infile loaation since
most of the analysis is done in subdirectories and the actual support
files are in ../.'''
infile = os.path.abspath(infile)
foo = infile.strip('@')
try:
pyfits.open(foo)
except IOError:
return [item.strip() for item in open(foo)]
return [infile]
def resolve_fits_files(infile):
'''This function returns a properly formatted infile loaation since
most of the analysis is done in subdirectories and the actual support
files are in ../.'''
infile = os.path.abspath(infile)
foo = infile.strip('@')
try:
pyfits.open(foo)
except IOError:
return [item.strip() for item in open(foo)]
return [infile]
def getInteractiveFigureFromCSPEC(cspecfile,**kwargs):
trigTime = dataHandling.getTriggerTime(cspecfile)
f = pyfits.open(cspecfile)
if('DETNAM' in f['SPECTRUM'].header.keys()):
detector = transformGBMdetname(f['SPECTRUM'].header['DETNAM'])
elif('LLECUT' in f['SPECTRUM'].header.keys()):
detector = "LAT"
elif('TELESCOP' in f['SPECTRUM'].keys()):
detector = f['SPECTRUM'].header['TELESCOP']
else:
detector = 'Unknown'
pass
s = f['SPECTRUM']
d = s.data[(s.data.field('QUALITY')==0)]
#Avoid overflow and underflow channels
counts = d.field('COUNTS')[:,3:126]
met = d.field('TIME')-trigTime
exposure = d.field('EXPOSURE')
N = len(met)
LC = np.zeros(N)
for i in range(N):
LC[i]=counts[i].sum()
mask = (exposure > 0)
LC[mask] /= exposure[mask]
kwargs['xlabel'] = "Time since trigger"
kwargs['ylabel'] = "Counts/s"
kwargs['xoffset'] = trigTime
kwargs['title'] = detector
interactiveFigure = InteractiveFigure(met,LC,**kwargs)
f.close()
return interactiveFigure
def gtdiffrsp_mp(bins, SCFile, EVFile, OutFile, SaveTemp, SrcModel,IRF):
'''This function looks at the start and stop times in an event file
and splits the time into chunks. It then submits jobs based upon
those start and stop times.'''
print "Opening event file to determine break points..."
hdulist = pyfits.open(EVFile)
tstart = hdulist[0].header['TSTART']
tstop = hdulist[0].header['TSTOP']
hdulist.close()
starts, step = np.linspace(tstart,tstop,bins,endpoint=False, retstep=True)
stops = starts + step
scfiles = [SCFile for st in starts]
evfiles = [EVFile for st in starts]
srcmdls = [SrcModel for st in starts]
irfs = [IRF for st in starts]
pool = Pool(processes=bins)
times = np.array([starts,stops,scfiles,evfiles,srcmdls,irfs])
print "Spawning {} jobs...".format(bins)
tempfilenames = pool.map(diffrsp,times.transpose())
print "Combining temporary files..."
eventsum(tempfilenames, OutFile, SaveTemp)
def __init__(self, filteredFt1, ft2, triggerName, triggertime, outdir):
global active
if (active):
self.ft1 = filteredFt1
self.ft2 = ft2
#Read the cuts from the filtered ft1 file
f = pyfits.open(self.ft1)
h = f[0].header
self.ra = float(h['_ROI_RA'])
self.dec = float(h['_ROI_DEC'])
self.roi = float(h['_ROI_RAD'])
self.emin = float(h['_EMIN'])
self.emax = float(h['_EMAX'])
self.zmax = float(h['_ZMAX'])
self.irf = str(h['_IRF'])
f.close()
self.triggertime = triggertime
self.grbname = triggerName
self.outdir = outdir
else:
raise RuntimeError(
"BKGE is not available in your system, you cannot use it!")
def getInteractiveFigureFromCSPEC(cspecfile, **kwargs):
trigTime = dataHandling.getTriggerTime(cspecfile)
f = pyfits.open(cspecfile)
if ('DETNAM' in f['SPECTRUM'].header.keys()):
detector = transformGBMdetname(f['SPECTRUM'].header['DETNAM'])
elif ('LLECUT' in f['SPECTRUM'].header.keys()):
detector = "LAT"
elif ('TELESCOP' in f['SPECTRUM'].keys()):
detector = f['SPECTRUM'].header['TELESCOP']
else:
detector = 'Unknown'
pass
s = f['SPECTRUM']
d = s.data[(s.data.field('QUALITY') == 0)]
#Avoid overflow and underflow channels
counts = d.field('COUNTS')[:, 3:126]
met = d.field('TIME') - trigTime
exposure = d.field('EXPOSURE')
N = len(met)
LC = np.zeros(N)
for i in range(N):
LC[i] = counts[i].sum()
mask = (exposure > 0)
LC[mask] /= exposure[mask]
kwargs['xlabel'] = "Time since trigger"
kwargs['ylabel'] = "Counts/s"
kwargs['xoffset'] = trigTime
kwargs['title'] = detector
interactiveFigure = InteractiveFigure(met, LC, **kwargs)
f.close()
return interactiveFigure
def expsum(filenames, Outfile, SaveTemp):
'''This function takes a list of exposure maps and adds them together.
If there is only one file to be added, it just copies it to the
outfile. If there is more than one, it uses pyfits to open them all
up, sum the first hdus (the data) and then replaces the first file's
primary hdu with this summed data and writes it to the output file.'''
if len(filenames) <= 1:
subprocess.call(["cp", filenames[0], Outfile])
else:
expmap_files = [pyfits.open(filename) for filename in filenames]
summed_expmap_hdu = (np.array([expmap_file[0].data for expmap_file in expmap_files])).sum(axis=0)
expmap_files[0][0].data = summed_expmap_hdu
expmap_files[0][0].update_header()
expmap_files[0].writeto(Outfile, clobber='True')
for expmap_file in expmap_files: expmap_file.close()
if SaveTemp:
print "Did not delete the following temporary files:"
print filenames
else:
print "Deleting temporary files..."
for filename in filenames:
os.remove(filename)
def fitsToPNG(fitsfile, pngfile, vmin=None, vmax=None, **kwargs):
#Display the TS map
#figure = plt.figure()
tsfig = aplpy.FITSFigure(fitsfile, convention='calabretta')
tsfig.set_tick_labels_font(size='small')
tsfig.set_axis_labels_font(size='small')
if (vmin is not None and vmax is not None):
tsfig.show_colorscale(cmap='gist_heat',
aspect='auto',
vmin=float(vmin),
vmax=float(vmax))
else:
#Get maximum of image
f = pyfits.open(fitsfile)
maximum = f[0].data.max()
f.close()
tsfig.show_colorscale(cmap='gist_heat',
aspect='auto',
vmin=0,
vmax=float(maximum))
# Modify the tick labels for precision and format
tsfig.tick_labels.set_xformat('ddd.dd')
tsfig.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
tsfig.show_grid()
tsfig.add_colorbar()
if ('sources' in kwargs.keys()):
sources = kwargs['sources']
for src in sources:
tsfig.add_label(float(src[1]),
float(src[2]),
"%s" % src[0],
relative=False,
weight='bold',
color='green',
size='x-small',
verticalalignment='top',
horizontalalignment='left')
tsfig.show_markers([float(src[1])], [float(src[2])],
edgecolor='green',
marker='x')
pass
if ('ra' in kwargs.keys()):
ra = float(kwargs['ra'])
dec = float(kwargs['dec'])
tsfig.show_markers([ra], [dec],
edgecolor='cyan',
facecolor='cyan',
marker='x',
s=120,
alpha=0.5,
linewidth=3)
pass
#figure.canvas.draw()
tsfig.save(pngfile)
def merge_results(self):
tsmap = pyfits.open(self.tpl_file)
tsmap[0].data = num.zeros(tsmap[0].data.shape, dtype=num.float)
for subdir in self.subdirs:
result_file = os.path.join(subdir, 'ts_results.dat')
try:
ii, jj, ra, dec, ts_vals = read_data(result_file)
except:
continue
for i, j, ts in zip(ii, jj, ts_vals):
tsmap[0].data[int(j)][int(i)] = ts
tsmap.writeto(self.pars['outfile'], clobber=True)
def merge_results(self):
tsmap = pyfits.open(self.tpl_file)
tsmap[0].data = num.zeros(tsmap[0].data.shape, dtype=num.float)
for subdir in self.subdirs:
result_file = os.path.join(subdir, 'ts_results.dat')
try:
ii, jj, ra, dec, ts_vals = read_data(result_file)
except:
continue
for i, j, ts in zip(ii, jj, ts_vals):
tsmap[0].data[int(j)][int(i)] = ts
tsmap.writeto(self.pars['outfile'], clobber=True)
def displayImage(self):
self.image = aplpy.FITSFigure(self.skyimage,
convention='calabretta',
figure=self.figure,
subplot=[0.1, 0.10, 0.40, 0.7],
label='sky image')
imageFits = pyfits.open(self.skyimage)
img = imageFits[0].data
# Apply grayscale mapping of image
if (not self.empty):
skm = self.image.show_colorscale(cmap='gist_heat',
vmin=0.1,
vmax=max(img.flatten()),
stretch='log')
else:
skm = self.image.show_colorscale(cmap='gist_heat',
vmin=0,
vmax=0.1)
imageFits.close()
# Modify the tick labels for precision and format
self.image.tick_labels.set_xformat('ddd.dd')
self.image.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
try:
self.image.show_grid()
except:
#show_grid throw an exception if the grid was already there
pass
pass
#Try to plot a cross at the source position
if (self.obj_ra is not None):
self.image.show_markers([float(self.obj_ra)],
[float(self.obj_dec)],
edgecolor='cyan',
facecolor='cyan',
marker='x',
s=120,
alpha=0.5,
linewidth=2)
self.figure.canvas.draw()
def __init__(self,**kwargs):
self.tstarts = []
self.tstops = []
self.counts = []
eventsCounter = EventsCounter(eventfile=_fixPath(kwargs['eventfile']))
timeBinsFile = pyfits.open(_fixPath(kwargs['timeBinsFile']))
for rowID in range(timeBinsFile["TIMEBINS"].data.shape[0]):
curTstart = timeBinsFile["TIMEBINS"].data[rowID][0]
curTstop = timeBinsFile["TIMEBINS"].data[rowID][1]
self.tstarts.append(curTstart)
self.tstops.append(curTstop)
#Count how many events are contained in the TTE file between tstart and tstop
self.counts.append(eventsCounter.getNevents(curTstart,curTstop))
pass
timeBinsFile.close()
def __init__(self, **kwargs):
self.tstarts = []
self.tstops = []
self.counts = []
eventsCounter = EventsCounter(eventfile=_fixPath(kwargs['eventfile']))
timeBinsFile = pyfits.open(_fixPath(kwargs['timeBinsFile']))
for rowID in range(timeBinsFile["TIMEBINS"].data.shape[0]):
curTstart = timeBinsFile["TIMEBINS"].data[rowID][0]
curTstop = timeBinsFile["TIMEBINS"].data[rowID][1]
self.tstarts.append(curTstart)
self.tstops.append(curTstop)
#Count how many events are contained in the TTE file between tstart and tstop
self.counts.append(eventsCounter.getNevents(curTstart, curTstop))
pass
timeBinsFile.close()
def initWithCSPEC(self, cspec):
f = pyfits.open(cspec)
self.inputFile = 'cspec'
self.data = f["SPECTRUM", 1].data.copy()
cspectstarts = self.data.field("TIME")
cspectstops = self.data.field("ENDTIME")
cspectelapse = cspectstops - cspectstarts
#Counts or rates?
try:
cspeccounts = self.data.field("COUNTS")
except:
rates = self.data.field("RATE")
cspeccounts = map(lambda x: x[0] * x[1], zip(rates, cspectelapse))
pass
self.tstarts = numpy.array(cspectstarts)
self.tstops = numpy.array(cspectstops)
self.counts = numpy.array(map(lambda x: numpy.sum(x), cspeccounts))
#Place zeros where QUALITY is > 0 (bad data)
self.goodDataMask = (f["SPECTRUM"].data.field('QUALITY') == 0)
self.counts[~self.goodDataMask] = 0
f.close()
def initWithCSPEC(self,cspec):
f = pyfits.open(cspec)
self.inputFile = 'cspec'
self.data = f["SPECTRUM",1].data.copy()
cspectstarts = self.data.field("TIME")
cspectstops = self.data.field("ENDTIME")
cspectelapse = cspectstops-cspectstarts
#Counts or rates?
try:
cspeccounts = self.data.field("COUNTS")
except:
rates = self.data.field("RATE")
cspeccounts = map(lambda x:x[0]*x[1],zip(rates,cspectelapse))
pass
self.tstarts = numpy.array(cspectstarts)
self.tstops = numpy.array(cspectstops)
self.counts = numpy.array(map(lambda x:numpy.sum(x),cspeccounts))
#Place zeros where QUALITY is > 0 (bad data)
self.goodDataMask = (f["SPECTRUM"].data.field('QUALITY')==0)
self.counts[~self.goodDataMask] = 0
f.close()
def fitsToPNG(fitsfile,pngfile,vmin=None,vmax=None,**kwargs):
#Display the TS map
#figure = plt.figure()
tsfig = aplpy.FITSFigure(fitsfile,convention='calabretta')
tsfig.set_tick_labels_font(size='small')
tsfig.set_axis_labels_font(size='small')
if(vmin is not None and vmax is not None):
tsfig.show_colorscale(cmap='gist_heat',aspect='auto',vmin=float(vmin),vmax=float(vmax))
else:
#Get maximum of image
f = pyfits.open(fitsfile)
maximum = f[0].data.max()
f.close()
tsfig.show_colorscale(cmap='gist_heat',aspect='auto',vmin=0,vmax=float(maximum))
# Modify the tick labels for precision and format
tsfig.tick_labels.set_xformat('ddd.dd')
tsfig.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
tsfig.show_grid()
tsfig.add_colorbar()
if('sources' in kwargs.keys()):
sources = kwargs['sources']
for src in sources:
tsfig.add_label(float(src[1]),float(src[2]), "%s" % src[0],
relative=False,weight='bold',color='green', size='x-small',
verticalalignment='top', horizontalalignment='left')
tsfig.show_markers([float(src[1])],[float(src[2])],edgecolor='green',marker='x')
pass
if('ra' in kwargs.keys()):
ra = float(kwargs['ra'])
dec = float(kwargs['dec'])
tsfig.show_markers([ra],[dec],edgecolor='cyan',facecolor='cyan',marker='x',s=120,alpha=0.5,linewidth=3)
pass
#figure.canvas.draw()
tsfig.save(pngfile)
def displayImage(self):
self.image = aplpy.FITSFigure(self.skyimage,convention='calabretta',
figure=self.figure,
subplot=[0.1,0.10,0.40,0.7],
label='sky image')
imageFits = pyfits.open(self.skyimage)
img = imageFits[0].data
# Apply grayscale mapping of image
if(not self.empty):
skm = self.image.show_colorscale(cmap='gist_heat',vmin=0.1,
vmax=max(img.flatten()),
stretch='log')
else:
skm = self.image.show_colorscale(cmap='gist_heat',vmin=0,
vmax=0.1)
imageFits.close()
# Modify the tick labels for precision and format
self.image.tick_labels.set_xformat('ddd.dd')
self.image.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
try:
self.image.show_grid()
except:
#show_grid throw an exception if the grid was already there
pass
pass
#Try to plot a cross at the source position
if(self.obj_ra is not None):
self.image.show_markers([float(self.obj_ra)], [float(self.obj_dec)], edgecolor='cyan', facecolor='cyan',marker='x', s=120, alpha=0.5,linewidth=2)
self.figure.canvas.draw()
def __init__(self,filteredFt1,ft2,triggerName,triggertime,outdir):
global active
if(active):
self.ft1 = filteredFt1
self.ft2 = ft2
#Read the cuts from the filtered ft1 file
f = pyfits.open(self.ft1)
h = f[0].header
self.ra = float(h['_ROI_RA'])
self.dec = float(h['_ROI_DEC'])
self.roi = float(h['_ROI_RAD'])
self.emin = float(h['_EMIN'])
self.emax = float(h['_EMAX'])
self.zmax = float(h['_ZMAX'])
self.irf = str(h['_IRF'])
f.close()
self.triggertime = triggertime
self.grbname = triggerName
self.outdir = outdir
else:
raise RuntimeError("BKGE is not available in your system, you cannot use it!")
def run(**kwargs):
if (len(kwargs.keys()) == 0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('filteredeventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
expomap = thisCommand.getParValue('expomap')
ltcube = thisCommand.getParValue('ltcube')
xmlmodel = thisCommand.getParValue('xmlmodel')
showmodelimage = thisCommand.getParValue('showmodelimage')
optimize = thisCommand.getParValue('optimizeposition')
spectralfiles = thisCommand.getParValue('spectralfiles')
tsmin = float(thisCommand.getParValue('tsmin'))
skymap = thisCommand.getParValue('skymap')
liketype = thisCommand.getParValue('liketype')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
flemin = thisCommand.getParValue('flemin')
flemax = thisCommand.getParValue('flemax')
clul = float(thisCommand.getParValue('clul'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %
(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
assert clul < 1.0, "The confidence level for the upper limit (clul) must be < 1"
from GtBurst import dataHandling
from GtBurst.angularDistance import getAngularDistance
LATdata = dataHandling.LATData(eventfile, rspfile, ft2file)
try:
if (liketype == 'unbinned'):
outfilelike, sources = LATdata.doUnbinnedLikelihoodAnalysis(
xmlmodel,
tsmin,
expomap=expomap,
ltcube=ltcube,
emin=flemin,
emax=flemax,
clul=clul)
else:
#Generation of spectral files and optimization of the position is
#not supported yet for binned analysis
if (spectralfiles == 'yes'):
print(
"\nWARNING: you specified spectralfiles=yes, but the generation of spectral files is not supported for binned analysis\n"
)
spectralfiles = 'no'
if (optimize == 'yes'):
print(
"\nWARNING: you specified optimize=yes, but position optimization is not supported for binned analysis\n"
)
optimize = 'no'
outfilelike, sources = LATdata.doBinnedLikelihoodAnalysis(
xmlmodel,
tsmin,
expomap=expomap,
ltcube=ltcube,
emin=flemin,
emax=flemax,
clul=clul)
except GtBurstException as gt:
raise gt
except:
raise
#Transfer information on the source from the input to the output XML
irf = dataHandling._getParamFromXML(xmlmodel, 'IRF')
ra = dataHandling._getParamFromXML(xmlmodel, 'RA')
dec = dataHandling._getParamFromXML(xmlmodel, 'DEC')
name = dataHandling._getParamFromXML(xmlmodel, 'OBJECT')
try:
grb = filter(lambda x: x.name.lower().find(name.lower()) >= 0,
sources)[0]
grb_TS = grb.TS
except:
#A model without GRB
print("\nWarning: no GRB in the model!")
grb_TS = -1
pass
if (irf is None):
print(
"\n\nWARNING: could not read IRF from XML file. Be sure you know what you are doing..."
)
else:
dataHandling._writeParamIntoXML(outfilelike,
IRF=irf,
OBJECT=name,
RA=ra,
DEC=dec)
pass
if (spectralfiles == 'yes'):
phafile, rspfile, bakfile = LATdata.doSpectralFiles(outfilelike)
pass
localizationMessage = ''
bestra = ''
bestdec = ''
poserr = ''
distance = ''
if (optimize == 'yes'):
sourceName = name
#If the TS for the source is above tsmin, optimize its position
#grb = filter(lambda x:x.name.lower().find(sourceName.lower())>=0,sources)[0]
if (math.ceil(grb_TS) >= tsmin):
try:
bestra, bestdec, poserr = LATdata.optimizeSourcePosition(
outfilelike, sourceName)
except:
raise GtBurstException(
207,
"gtfindsrc execution failed. Were the source detected in the likelihood step?"
)
else:
localizationMessage += "\nNew localization from gtfindsrc:\n\n"
localizationMessage += "(R.A., Dec) = (%6.3f, %6.3f)\n" % (
bestra, bestdec)
localizationMessage += "68 %s containment radius = %6.3f\n" % (
'%', poserr)
localizationMessage += "90 %s containment radius = %6.3f\n" % (
'%', 1.41 * poserr)
distance = getAngularDistance(float(ra), float(dec),
float(bestra), float(bestdec))
localizationMessage += "Distance from initial position = %6.3f\n\n" % (
distance)
localizationMessage += "NOTE: this new localization WILL NOT be used by default. If you judge"
localizationMessage += " it is a better localization than the one you started with, update the"
localizationMessage += " coordinates yourself and re-run the likelihood\n"
pass
pass
if (figure is not None and skymap is not None and showmodelimage == 'yes'):
#Now produce the binned exposure map (needed in order to display the fitted model as an image)
modelmapfile = LATdata.makeModelSkyMap(outfilelike)
#Display point sources in the image, and report in the table
#all 3FGL sources with TS > 9 + always the GRB, independently of its TS
detectedSources = []
grbFlux = 1e-13
for src in sources:
weight = 'bold'
if (src.type == 'PointSource'):
if (src.TS > 4):
detectedSources.append(src)
if (src.name.find('3FGL') < 0):
#GRB
grbFlux = src.flux
pass
pass
pass
#Display the counts map
from GtBurst import aplpy
import matplotlib.pyplot as plt
figure.clear()
orig = aplpy.FITSFigure(skymap,
convention='calabretta',
figure=figure,
subplot=[0.1, 0.1, 0.45, 0.7])
vmax = max(pyfits.open(skymap)[0].data.flatten())
nEvents = numpy.sum(pyfits.open(skymap)[0].data)
telapsed = pyfits.open(eventfile)[0].header['TSTOP'] - pyfits.open(
eventfile)[0].header['TSTART']
orig.set_tick_labels_font(size='small')
orig.set_axis_labels_font(size='small')
orig.show_colorscale(cmap='gist_heat',
vmin=0.1,
vmax=max(vmax, 0.11),
stretch='log',
aspect='auto')
# Modify the tick labels for precision and format
orig.tick_labels.set_xformat('ddd.dd')
orig.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
orig.show_grid()
#Renormalize the modelmapfile to the flux of the grb
f = pyfits.open(modelmapfile, 'update')
f[0].data = f[0].data / numpy.max(f[0].data) * nEvents / telapsed
print(numpy.max(f[0].data))
f.close()
img = aplpy.FITSFigure(modelmapfile,
convention='calabretta',
figure=figure,
subplot=[0.55, 0.1, 0.4, 0.7])
img.set_tick_labels_font(size='small')
img.set_axis_labels_font(size='small')
#vmax = max(pyfits.open(modelmapfile)[0].data.flatten())
img.show_colorscale(cmap='gist_heat', aspect='auto', stretch='log')
for src in detectedSources:
img.add_label(float(src.ra),
float(src.dec),
"%s\n(ts = %i)" % (src.name, int(math.ceil(src.TS))),
relative=False,
weight=weight,
color='green',
size='small')
pass
# Modify the tick labels for precision and format
img.tick_labels.set_xformat('ddd.dd')
img.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
img.show_grid()
#ax = figure.add_axes([0.1,0.72,0.85,0.25],frame_on=False)
#ax.xaxis.set_visible(False)
#ax.yaxis.set_visible(False)
#col_labels =['Source Name','TS','Energy flux','Photon index']
#table_vals = map(lambda x:[x.name,"%i" %(int(math.ceil(x.TS))),
# "%s +/- %s" % (x.flux,x.fluxError),
# "%s +/- %s" % (x.photonIndex,x.photonIndexError)],detectedSources)
#
#if(len(table_vals)>0):
# the_table = ax.table(cellText=table_vals,
# colLabels=col_labels,
# loc='upper center')
figure.canvas.draw()
figure.savefig("likelihood_results.png")
pass
if (figure is not None):
#Assume we have an X server running
#Now display the results
likemsg = "Log(likelihood) = %s" % (LATdata.logL)
displayResults(
figure.canvas._tkcanvas, LATdata.resultsStrings + "\n" + likemsg +
"\n" + localizationMessage)
print(localizationMessage)
return 'likexmlresults', outfilelike, 'TS', grb_TS, 'bestra', bestra, 'bestdec', bestdec, 'poserr', poserr, 'distance', distance, 'sources', sources
def run(**kwargs):
if(len(kwargs.keys())==0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('filteredeventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
tsmin = float(thisCommand.getParValue('tsmin'))
fixphindex = thisCommand.getParValue('fixphindex')
expomap = thisCommand.getParValue('expomap')
ltcube = thisCommand.getParValue('ltcube')
xmlmodel = thisCommand.getParValue('xmlmodel')
sedtxt = thisCommand.getParValue('sedtxt')
energybins = thisCommand.getParValue('energybins')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
from GtBurst import dataHandling
from GtBurst.angularDistance import getAngularDistance
LATdata = dataHandling.LATData(eventfile,rspfile,ft2file)
try:
outfilelike, sources = LATdata.doUnbinnedLikelihoodAnalysis(xmlmodel,tsmin=20,expomap=expomap,ltcube=ltcube)
except GtBurstException as gt:
raise gt
except:
raise
#Now produce a pha1 file (just to compute the total exposure)
pha1file = LATdata.binByEnergy(2)
totalExposure = pyfits.getheader(pha1file,'SPECTRUM').get("EXPOSURE")
print("\nTotal exposure: %s s" %(totalExposure))
#Transfer information on the source from the input to the output XML
irf = dataHandling._getParamFromXML(xmlmodel,'IRF')
ra = dataHandling._getParamFromXML(xmlmodel,'RA')
dec = dataHandling._getParamFromXML(xmlmodel,'DEC')
sourceName = dataHandling._getParamFromXML(xmlmodel,'OBJECT')
if(irf is None):
print("\n\nWARNING: could not read IRF from XML file. Be sure you know what you are doing...")
else:
dataHandling._writeParamIntoXML(outfilelike,IRF=irf,OBJECT=sourceName,RA=ra,DEC=dec)
pass
#Make a copy of the output XML file and freeze all parameters except the source
tree = ET.parse(outfilelike)
root = tree.getroot()
phIndex = -2.0
for source in root.findall('source'):
if(source.get('name')!=sourceName):
#Freeze all parameters
for spectrum in source.findall('spectrum'):
for param in spectrum.findall('parameter'):
param.set('free',"%s" % 0)
pass
pass
else:
if(fixphindex.lower()!='no' and fixphindex!=False):
#Fix photon index
for spectrum in source.findall('spectrum'):
for param in spectrum.findall('parameter'):
if(param.get('name')=='Index'):
if(fixphindex.lower()!='prefit'):
try:
value = float(fixphindex)
except:
raise ValueError("The value for the photon index (%s) is not a float. Value not recognized." % fixphindex)
print("\n\nFixing photon index to the provided value (%s) \n\n" %(value))
param.set('value',"%s" % value)
else:
print("\n\nFixing photon index to the best fit value on the whole energy range\n\n")
param.set('free',"%s" % 0)
phIndex = float(param.get('value'))
pass
pass
pass
pass
f = open("__temporary_XML.xml",'w+')
tree.write(f)
f.write('''<!-- OBJECT=%s -->
<!-- DEC=%s -->
<!-- RA=%s -->
<!-- IRF=%s -->\n''' %(sourceName,ra,dec,irf))
f.close()
#Now for each energy band, make a likelihood and compute the flux
f = pyfits.open(eventfile)
#Take the list in inverse order so I will rebin at low energies, instead that at high energies
energies = numpy.array(sorted(f['EVENTS'].data.ENERGY)[::-1])
totalInputCounts = len(energies)
f.close()
if(energybins is not None):
energyBoundaries = map(lambda x:float(x),energybins.split(','))
else:
energyBoundaries = LikelihoodComponent.optimizeBins(LATdata.like1,energies,sourceName,minTs=tsmin,minEvt=3)
print("\nEnergy boundaries:")
for i,ee1,ee2 in zip(range(len(energyBoundaries)-1),energyBoundaries[:-1],energyBoundaries[1:]):
print("%02i: %10s - %10s" %(i+1,ee1,ee2))
pass
print("\n")
print("\nNumber of energy bins: %s\n" %(len(energyBoundaries)-1))
fluxes = numpy.zeros(len(energyBoundaries)-1)
fluxes_errors = numpy.zeros(len(energyBoundaries)-1)
phfluxes = numpy.zeros(len(energyBoundaries)-1)
phfluxes_errors = numpy.zeros(len(energyBoundaries)-1)
TSs = numpy.zeros(len(energyBoundaries)-1)
phIndexes = numpy.zeros(len(energyBoundaries)-1)
totalCounts = 0
for i,e1,e2 in zip(range(len(fluxes)),energyBoundaries[:-1],energyBoundaries[1:]):
thisLATdata = dataHandling.LATData(eventfile,rspfile,ft2file,root="SED_%s-%s" %(e1,e2))
#Further cut in the energy range for this Band
#Remove the version _vv from the name of the irf
cleanedIrf = "_".join(LATdata.irf.split("_")[:-1])
outf,thisCounts = thisLATdata.performStandardCut(LATdata.ra,LATdata.dec,LATdata.rad,cleanedIrf,
LATdata.tmin,LATdata.tmax,e1,e2,180,gtmktime=False,roicut=False)
totalCounts += thisCounts
#Perform the likelihood analysis
outfilelike, sources = thisLATdata.doUnbinnedLikelihoodAnalysis("__temporary_XML.xml",
tsmin=tsmin,
dogtdiffrsp=False,
expomap=expomap,
ltcube=ltcube)
source = filter(lambda x:x.name==sourceName,sources)[0]
if(source.flux.find("<")>=0):
#This is an upper limit
source.flux = float(source.flux.split("<")[1].strip())
source.fluxError = -1
source.photonFlux = float(source.photonFlux.split("<")[1].strip())
source.photonFluxError = -1
pass
fluxes[i] = source.flux
fluxes_errors[i] = source.fluxError
phfluxes[i] = source.photonFlux
phfluxes_errors[i] = source.photonFluxError
TSs[i] = float(source.TS)
phIndexes[i] = float(source.photonIndex)
pass
if(totalCounts!=totalInputCounts):
raise RuntimeError("We have losted somewhere %s counts!" %(totalInputCounts-totalCounts))
pass
#Now compute the SED points
MeV2Erg = 1.60217657e-6
ee1 = numpy.array(energyBoundaries[:-1])
ee2 = numpy.array(energyBoundaries[1:])
ee = (ee1+ee2)/2.0
de = (ee2-ee1)
#Use the photon index of the total fit to compute the mean energy
pow = numpy.power
meanEnergies = numpy.array(map(lambda x:computeMeanEnergy(x[0],x[1],x[2]),zip(phIndexes,ee1,ee2)))
nuFnu = phfluxes / de * pow(meanEnergies,2.0) * MeV2Erg
nuFnuError = phfluxes_errors / de * pow(meanEnergies,2.0) * MeV2Erg
#Print the results of the SED
fw = open(sedtxt,'w+')
fw.write("#Energy_min Energy_Max Flux Flux_error PhotonFlux PhotonFluxError TS nuFnu_energy nuFnu_energy_negerr nuFnu_energy_poserr nuFnu_value nuFnu_value_error\n")
fw.write("#MeV MeV erg/cm2/s erg/cm2/s ph/cm2/s ph/cm2/s - MeV MeV MeV erg/cm2/s erg/cm2/s\n")
fw.write("#Total exposure: %s s\n" %(totalExposure))
for e1,e2,f,fe,ph,phe,ts,ne,nee1,nee2,nuF,nuFe in zip(energyBoundaries[:-1],energyBoundaries[1:],fluxes,fluxes_errors,phfluxes,phfluxes_errors,TSs,meanEnergies,meanEnergies-ee1,ee2-meanEnergies,nuFnu,nuFnuError):
fw.write("%s %s %s %s %s %s %s %s %s %s %s %s\n" %(e1,e2,f,fe,ph,phe,ts,ne,nee1,nee2,nuF,nuFe))
print("%10s - %10s MeV -> %g +/- %g erg/cm2/s, %g +/- %g ph/cm2/s, TS = %s" %(e1,e2,f,fe,ph,phe,ts))
pass
fw.close()
if(figure is not None):
#Display the SED
if(os.environ.get('DISPLAY') is None):
os.environ.set('DISPLAY','/dev/null')
import matplotlib
matplotlib.use('Agg')
pass
import matplotlib.pyplot as plt
try:
figure.clear()
except:
print("Creating new figure...")
try:
figure = plt.figure()
except:
plt.switch_backend('Agg')
figure = plt.figure()
pass
sub = figure.add_subplot(111)
sub.errorbar(meanEnergies,nuFnu,xerr=[meanEnergies-ee1,ee2-meanEnergies],yerr=nuFnuError,fmt='.')
sub.set_xlabel("Energy (MeV)")
sub.set_ylabel("Flux (erg/cm2/s)")
sub.loglog()
sub.set_xlim(0.9*min(ee1),1.1*max(ee2))
figure.canvas.draw()
figure.savefig("%s.png" % ".".join(sedtxt.split(".")[0:-1]))
pass
return 'sedtxt', sedtxt
def initWithTTE(self, tte):
f = pyfits.open(tte)
self.inputFile = 'TTE'
self.data = f["EVENTS", 1].data.copy()
self.eventTimes = numpy.array(list(self.data.field('TIME')))
f.close()
def gtltcube_mp(bins, SCFile, EVFile, OutFile, SaveTemp, zmax):
'''This functions looks at a spacecraft file and splits the time into
chunks that match the bin edges in the spacecraft file. It then
submits jobs based upon those start and stop times. This is to
make the resulting files as close to the original as possible.
Note that this assumes you are using the full time period in your
spacecraft file.'''
verbose = False
print "Opening event file to determine start and stop times..."
evfile = pyfits.open(EVFile, mode='readonly')
tstart = evfile[0].header['TSTART']
tstop = evfile[0].header['TSTOP']
gti_data = evfile[2].data
print "Opening SC file to determine break points..."
hdulist = pyfits.open(SCFile, mode='readonly')
scdata = hdulist[1].data
hdulist.close()
scstart = scdata.field('START')
scstop = scdata.field('STOP')
time_filter = (tstart <= scstart) & (scstop <= tstop)
redo = True
print "Checking for good times in the event file..."
while redo:
redo = False
scstartssplit = np.array_split(scstart[time_filter], int(bins))
scstopssplit = np.array_split(scstop[time_filter], bins)
#Explicitly set the first and last point to the values in the evfile header
scstartssplit[0][0] = tstart
scstopssplit[-1][-1] = tstop
starts = [st[0] for st in scstartssplit]
stops = [st[-1] for st in scstopssplit]
for interval in zip(starts, stops):
if verbose:
print "Looking at interval", interval[0], "to", interval[1]
good_times = False
#grrrr. some bug in pyfits doesn't let me do this the python way...
for gti_i in range(len(gti_data)):
if (not good_times):
if verbose:
print " Checking gti", gti_data[gti_i][
'START'], "to", gti_data[gti_i]['STOP']
gti_starts = interval[0] <= gti_data[gti_i][
'START'] <= interval[1]
gti_stops = interval[0] < gti_data[gti_i][
'STOP'] <= interval[1]
if verbose:
print " Does this gti start inside this interval? ", gti_starts
if verbose:
print " Does this gti stop inside this interval? ", gti_stops
good_times = gti_starts or gti_stops
if verbose: print
if verbose:
print " Are there good times inside this interval? ", good_times
if not good_times:
redo = True
if verbose: print
if redo:
if bins <= 1:
print "No good time intervals found. Bailing..."
sys.exit(1)
print "One (or more) of the slices doesn't have a GTI."
print "Reducing the number of threads from ", bins, "to", bins - 1
bins -= 1
scfiles = [SCFile for st in scstartssplit]
evfiles = [EVFile for st in scstartssplit]
zmaxes = [zmax for st in scstartssplit]
pool = Pool(processes=bins)
times = np.array([starts, stops, scfiles, evfiles, zmaxes])
print "Spawning {} jobs...".format(bins)
tempfilenames = pool.map(ltcube, times.transpose())
print "Combining temporary files..."
ltsum(tempfilenames, OutFile, SaveTemp)
def run(**kwargs):
if (len(kwargs.keys()) == 0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
ra = thisCommand.getParValue('ra')
dec = thisCommand.getParValue('dec')
particlemodel = thisCommand.getParValue('particle_model')
galacticmodel = thisCommand.getParValue('galactic_model')
sourcemodel = thisCommand.getParValue('source_model')
filteredeventfile = thisCommand.getParValue('filteredeventfile')
xmlmodel = thisCommand.getParValue('xmlmodel')
triggername = thisCommand.getParValue('triggername')
ft2file = thisCommand.getParValue('ft2file')
fglmode = thisCommand.getParValue('fgl_mode')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %
(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
#Get the IRF from the event file
try:
f = pyfits.open(filteredeventfile)
except:
raise GtBurstException(
31, "Could not open filtered event file %s" % (filteredeventfile))
tstart = float(f[0].header['_TMIN'])
tstop = float(f[0].header['_TMAX'])
irf = str(f[0].header['_IRF'])
ra = float(f[0].header['_ROI_RA'])
dec = float(f[0].header['_ROI_DEC'])
roi = float(f[0].header['_ROI_RAD'])
#Lookup table for the models
models = {}
if (particlemodel == 'isotr with pow spectrum'):
models[
'isotr with pow spectrum'] = LikelihoodComponent.IsotropicPowerlaw(
)
elif (particlemodel == 'isotr template'):
models['isotr template'] = LikelihoodComponent.IsotropicTemplate(irf)
pass
if (galacticmodel == 'template'):
models['template'] = LikelihoodComponent.GalaxyAndExtragalacticDiffuse(
irf, ra, dec, 2.5 * roi)
elif (galacticmodel == 'template (fixed norm.)'):
models[
'template (fixed norm.)'] = LikelihoodComponent.GalaxyAndExtragalacticDiffuse(
irf, ra, dec, 2.5 * roi)
models['template (fixed norm.)'].fixNormalization()
pass
deltat = numpy.sum(f['GTI'].data.field('STOP') -
f['GTI'].data.field('START'))
f.close()
triggertime = dataHandling.getTriggerTime(filteredeventfile)
if (irf.lower().find('source') >= 0 and particlemodel != 'isotr template'):
raise GtBurstException(
6,
"Do not use '%s' as model for the particle background in SOURCE class. Use '%s' instead."
% (particlemodel, 'isotropic template'))
modelsToUse = [
LikelihoodComponent.PointSource(ra, dec, triggername, sourcemodel)
]
if (particlemodel != 'none'):
if (particlemodel == 'bkge'):
if (ft2file is None or ft2file == ''):
raise ValueError(
"If you want to use the BKGE, you have to provide an FT2 file!"
)
modelsToUse.append(
LikelihoodComponent.BKGETemplate(filteredeventfile, ft2file,
tstart, tstop, triggername,
triggertime))
else:
modelsToUse.append(models[particlemodel])
if (galacticmodel != 'none'):
modelsToUse.append(models[galacticmodel])
xml = LikelihoodComponent.LikelihoodModel()
xml.addSources(*modelsToUse)
xml.writeXML(xmlmodel)
if (fglmode == 'complete'):
#Use a very large delta T so that
#all FGL sources are included in the
#xml model
deltat = 1e12
xml.add2FGLsources(ra, dec, float(roi) + 8.0, xmlmodel, deltat)
dataHandling._writeParamIntoXML(xmlmodel,
IRF=irf,
OBJECT=triggername,
RA=ra,
DEC=dec)
return 'xmlmodel', xmlmodel
def run(**kwargs):
if(len(kwargs.keys())==0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('filteredeventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
expomap = thisCommand.getParValue('expomap')
ltcube = thisCommand.getParValue('ltcube')
xmlmodel = thisCommand.getParValue('xmlmodel')
showmodelimage = thisCommand.getParValue('showmodelimage')
optimize = thisCommand.getParValue('optimizeposition')
spectralfiles = thisCommand.getParValue('spectralfiles')
tsmin = float(thisCommand.getParValue('tsmin'))
skymap = thisCommand.getParValue('skymap')
liketype = thisCommand.getParValue('liketype')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
flemin = thisCommand.getParValue('flemin')
flemax = thisCommand.getParValue('flemax')
clul = float(thisCommand.getParValue('clul'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
assert clul < 1.0, "The confidence level for the upper limit (clul) must be < 1"
from GtBurst import dataHandling
from GtBurst.angularDistance import getAngularDistance
LATdata = dataHandling.LATData(eventfile,rspfile,ft2file)
try:
if(liketype=='unbinned'):
outfilelike, sources = LATdata.doUnbinnedLikelihoodAnalysis(xmlmodel,tsmin,expomap=expomap,ltcube=ltcube,emin=flemin,emax=flemax, clul=clul)
else:
#Generation of spectral files and optimization of the position is
#not supported yet for binned analysis
if(spectralfiles=='yes'):
print("\nWARNING: you specified spectralfiles=yes, but the generation of spectral files is not supported for binned analysis\n")
spectralfiles = 'no'
if(optimize=='yes'):
print("\nWARNING: you specified optimize=yes, but position optimization is not supported for binned analysis\n")
optimize = 'no'
outfilelike, sources = LATdata.doBinnedLikelihoodAnalysis(xmlmodel,tsmin,expomap=expomap,ltcube=ltcube,emin=flemin,emax=flemax, clul=clul)
except GtBurstException as gt:
raise gt
except:
raise
#Transfer information on the source from the input to the output XML
irf = dataHandling._getParamFromXML(xmlmodel,'IRF')
ra = dataHandling._getParamFromXML(xmlmodel,'RA')
dec = dataHandling._getParamFromXML(xmlmodel,'DEC')
name = dataHandling._getParamFromXML(xmlmodel,'OBJECT')
try:
grb = filter(lambda x:x.name.lower().find(name.lower())>=0,sources)[0]
grb_TS = grb.TS
except:
#A model without GRB
print("\nWarning: no GRB in the model!")
grb_TS = -1
pass
if(irf is None):
print("\n\nWARNING: could not read IRF from XML file. Be sure you know what you are doing...")
else:
dataHandling._writeParamIntoXML(outfilelike,IRF=irf,OBJECT=name,RA=ra,DEC=dec)
pass
if(spectralfiles=='yes'):
phafile,rspfile,bakfile = LATdata.doSpectralFiles(outfilelike)
pass
localizationMessage = ''
bestra = ''
bestdec = ''
poserr = ''
distance = ''
if(optimize=='yes'):
sourceName = name
#If the TS for the source is above tsmin, optimize its position
#grb = filter(lambda x:x.name.lower().find(sourceName.lower())>=0,sources)[0]
if(math.ceil(grb_TS) >= tsmin):
try:
bestra,bestdec,poserr = LATdata.optimizeSourcePosition(outfilelike,sourceName)
except:
raise GtBurstException(207,"gtfindsrc execution failed. Were the source detected in the likelihood step?")
else:
localizationMessage += "\nNew localization from gtfindsrc:\n\n"
localizationMessage += "(R.A., Dec) = (%6.3f, %6.3f)\n" %(bestra,bestdec)
localizationMessage += "68 %s containment radius = %6.3f\n" %('%',poserr)
localizationMessage += "90 %s containment radius = %6.3f\n" %('%',1.41*poserr)
distance = getAngularDistance(float(ra),float(dec),float(bestra),float(bestdec))
localizationMessage += "Distance from initial position = %6.3f\n\n" %(distance)
localizationMessage += "NOTE: this new localization WILL NOT be used by default. If you judge"
localizationMessage += " it is a better localization than the one you started with, update the"
localizationMessage += " coordinates yourself and re-run the likelihood\n"
pass
pass
if(figure is not None and skymap is not None and showmodelimage=='yes'):
#Now produce the binned exposure map (needed in order to display the fitted model as an image)
modelmapfile = LATdata.makeModelSkyMap(outfilelike)
#Display point sources in the image, and report in the table
#all 3FGL sources with TS > 9 + always the GRB, independently of its TS
detectedSources = []
grbFlux = 1e-13
for src in sources:
weight = 'bold'
if(src.type=='PointSource'):
if(src.TS > 4):
detectedSources.append(src)
if(src.name.find('3FGL')<0):
#GRB
grbFlux = src.flux
pass
pass
pass
#Display the counts map
from GtBurst import aplpy
import matplotlib.pyplot as plt
figure.clear()
orig = aplpy.FITSFigure(skymap,convention='calabretta',
figure=figure,subplot=[0.1,0.1,0.45,0.7])
vmax = max(pyfits.open(skymap)[0].data.flatten())
nEvents = numpy.sum(pyfits.open(skymap)[0].data)
telapsed = pyfits.open(eventfile)[0].header['TSTOP']-pyfits.open(eventfile)[0].header['TSTART']
orig.set_tick_labels_font(size='small')
orig.set_axis_labels_font(size='small')
orig.show_colorscale(cmap='gist_heat',vmin=0.1,vmax=max(vmax,0.11),stretch='log',aspect='auto')
# Modify the tick labels for precision and format
orig.tick_labels.set_xformat('ddd.dd')
orig.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
orig.show_grid()
#Renormalize the modelmapfile to the flux of the grb
f = pyfits.open(modelmapfile,'update')
f[0].data = f[0].data/numpy.max(f[0].data)*nEvents/telapsed
print(numpy.max(f[0].data))
f.close()
img = aplpy.FITSFigure(modelmapfile,convention='calabretta',
figure=figure,subplot=[0.55,0.1,0.4,0.7])
img.set_tick_labels_font(size='small')
img.set_axis_labels_font(size='small')
#vmax = max(pyfits.open(modelmapfile)[0].data.flatten())
img.show_colorscale(cmap='gist_heat',aspect='auto',stretch='log')
for src in detectedSources:
img.add_label(float(src.ra),float(src.dec),
"%s\n(ts = %i)" %(src.name,int(math.ceil(src.TS))),
relative=False,weight=weight,
color='green', size='small')
pass
# Modify the tick labels for precision and format
img.tick_labels.set_xformat('ddd.dd')
img.tick_labels.set_yformat('ddd.dd')
# Display a grid and tweak the properties
img.show_grid()
#ax = figure.add_axes([0.1,0.72,0.85,0.25],frame_on=False)
#ax.xaxis.set_visible(False)
#ax.yaxis.set_visible(False)
#col_labels =['Source Name','TS','Energy flux','Photon index']
#table_vals = map(lambda x:[x.name,"%i" %(int(math.ceil(x.TS))),
# "%s +/- %s" % (x.flux,x.fluxError),
# "%s +/- %s" % (x.photonIndex,x.photonIndexError)],detectedSources)
#
#if(len(table_vals)>0):
# the_table = ax.table(cellText=table_vals,
# colLabels=col_labels,
# loc='upper center')
figure.canvas.draw()
figure.savefig("likelihood_results.png")
pass
if(figure is not None):
#Assume we have an X server running
#Now display the results
likemsg = "Log(likelihood) = %s" %(LATdata.logL)
displayResults(figure.canvas._tkcanvas, LATdata.resultsStrings + "\n" + likemsg + "\n" + localizationMessage)
print(localizationMessage)
return 'likexmlresults', outfilelike, 'TS', grb_TS, 'bestra', bestra, 'bestdec', bestdec, 'poserr', poserr, 'distance', distance,'sources', sources
def run(**kwargs):
if(len(kwargs.keys())==0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
# Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
cspec_in = None
try:
eventfile = thisCommand.getParValue('eventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
cspec_in = thisCommand.getParValue('cspec_in')
dt = thisCommand.getParValue('dt')
tstart = thisCommand.getParValue('tstart')
tstop = thisCommand.getParValue('tstop')
outfile = thisCommand.getParValue('cspecfile')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
#I import this here to save time if the user makes error in the command line
from GtBurst import dataHandling
message = Message(verbose)
#Load LLE data
message(" * Get energy binning from the response matrix...")
lleData = dataHandling.LLEData(eventfile,rspfile,ft2file)
message("\n done.")
#Make PHA2 with gtbin
tempPHA2filename = "__gtllebin__pha2.pha"
# This will use the same energy binning as in the EBOUNDS extension of the rspfile
message("\n * Run gtbindef and gtbin and bin in energy and time...\n")
# if you input a cspec file it will clone the time binning:
if cspec_in is not None:
tmpFile=pyfits.open(cspec_in)
message('=> Copying the binning from: %s' % cspec_in)
_SPECTRUM=tmpFile['SPECTRUM'].data
_TIME =_SPECTRUM.field('TIME')
_ENDTIME =_SPECTRUM.field('ENDTIME')
tempTimeBinFile="__tmpBinFileFromCSPEC.txt"
txt=''
for i in range(len(_TIME)): txt+='%s\t%s\n' %( _TIME[i],_ENDTIME[i])
file(tempTimeBinFile,'w').writelines(txt)
tempTimeBinFile_fits="__tmpBinFileFromCSPEC.fits"
lleData.gtbindef['bintype']='T'
lleData.gtbindef['binfile']=tempTimeBinFile
lleData.gtbindef['outfile']=tempTimeBinFile_fits
lleData.gtbindef.run()
lleData.binByEnergyAndTime(tempTimeBinFile_fits,tempPHA2filename)
else: lleData.binByEnergyAndTime(tstart,tstop,dt,tempPHA2filename)
message("\n done.")
#Transform the PHA2 in CSPEC
message("\n * Transform gtbin output in CSPEC format...")
pha2 = dataHandling.Spectra(tempPHA2filename)
#Set Poisson errors to true (this will make POISSERR=True in the output CSPEC file)
pha2.setPoisson(True)
pha2.write(outfile,format="CSPEC",trigtime=lleData.trigTime,clobber=clobber)
#Remove the temporary PHA2 file
os.remove(tempPHA2filename)
message("\n done.")
#Copy some keywords from the LLE file to the CSPEC file
message("\n * Updating keywords in the headers of the CSPEC file...")
dataHandling.fixHeaders(eventfile,outfile)
message("\n done.")
message("\n%s done!" %(thisCommand.name))
return 'cspecfile', outfile
def RSPweight(**kwargs):
'''
Weight the response matrices contained in the rsp file.
Usage:
RSPweight(eventfile=eventfile,timeBinsFile=timeBinsFile,rsp2file=rsp2file,
outfile=outfile,triggerTime=triggerTime)
where eventfile can be either a TTE/FT1 file or a CSPEC file.
'''
timeIntervals = TimeIntervals(**kwargs)
rsp2 = _fixPath(kwargs['rsp2file'])
out = _fixPath(kwargs['outfile'])
#The trigger time is only for printing purposes: if specified,
#all the messages from the program will contain time intervals referred
#to the trigger time, otherwise they will be in MET
if ('triggerTime' in kwargs.keys()):
trigger = kwargs['triggerTime']
else:
trigger = 0
#name for the output
outrsp = out
#Open files
rsp2File = pyfits.open(rsp2)
#Get instrument name
try:
instrument = pyfits.getval(_fixPath(kwargs['eventfile']),
"INSTRUME",
extname="EVENTS",
extver=1)
except:
try:
instrument = pyfits.getval(_fixPath(kwargs['eventfile']),
"INSTRUME",
extname="SPECTRUM",
extver=1)
except:
instrument = "UNKN-INSTRUME"
#For every interval contained in the time bins file
#find the applying matrices and compute the weight (if necessary)
nIntervals = len(timeIntervals.tstarts)
nMatrix = len(rsp2File)
createdRspNames = []
eventsCounter = EventsCounter(**kwargs)
for tstart, tstop, nEvents, intervalNumber in zip(timeIntervals.tstarts,
timeIntervals.tstops,
timeIntervals.counts,
range(nIntervals)):
print("\nInterval: %s - %s" % (tstart - trigger, tstop - trigger))
#find the RSP matrices falling in the current tstart-tstop interval
rspList = []
rspStarts = []
rspStops = []
firstResponse = True
for extNumber in range(nMatrix):
if (rsp2File[extNumber].name == "SPECRESP MATRIX"
or rsp2File[extNumber].name == "MATRIX"):
curHeader = rsp2File[extNumber].header
#Check that the instrument is the same of the TTE file
instrument2 = curHeader["INSTRUME"]
if (instrument2 != instrument):
print(
"WARNING: the Events file %s and the response matrix file %s refers to different instruments"
% (instrument, instrument2))
pass
#Find the start and stop time of the interval covered
#by this response matrix
headerStart = curHeader["TSTART"]
headerStop = curHeader["TSTOP"]
#The matrix cover the period going from the middle point between its start
#time and the start time of the previous matrix (or its start time if it is the
#first matrix of the file), and the middle point between its start time and its
#stop time (that is equal to the start time of the next one):
#
# tstart tstop
# |==================================|
# |--------------x---------------|-----------x-----------|----------x--..
#rspStart1 rspStop1= headerStart2 rspStop2= headerStart3 rspStop3
# rspStart2 rspStart3
#
#covered by: |m1 | m2 | m3|
#
if (firstResponse):
#This is the first matrix
if (extNumber == len(rsp2File) - 1):
#This is both the first both the last matrix
rspStart = headerStart
rspStop = headerStop
else:
rspStart = headerStart
rspStop = (headerStart + headerStop) / 2.0
firstResponse = False
pass
elif (extNumber == len(rsp2File) - 1):
#This is the last matrix
rspStart = prevRspStop
rspStop = headerStop
else:
rspStart = prevRspStop
rspStop = (headerStart + headerStop) / 2.0
pass
#Save the stop time for the next iteration of the loop
prevRspStop = rspStop
#Check if the current matrix covers at least a portion of the
#start-stop time interval
if (rspStop >= tstart and rspStart <= tstop):
#Found a matrix covering a part of the interval:
#adding it to the list:
rspList.append(fixMatrixHDU(rsp2File[extNumber]))
#Get the "true" start time of the time sub-interval covered by this matrix
trueRspStart = max(rspStart, tstart)
#Get the "true" stop time of the time sub-interval covered by this matrix
if (extNumber == len(rsp2File) - 1):
#Since there are no matrices after this one, this has to cover until the end of the interval
if (tstop > rspStop):
print(
"\nWARNING: RSPweight: The RSP file does not cover the required time interval."
)
print(
" The last response should cover from %s to %s, but we'll use it"
% (trueRspStart, rspStop))
print(
" to cover until the end of the time interval (%s)."
% (tstop))
trueRspStop = tstop
else:
trueRspStop = tstop
else:
trueRspStop = min(rspStop, tstop)
pass
#Save the true start and stop
rspStarts.append(trueRspStart)
rspStops.append(trueRspStop)
print(" Matr. in ext #%s covers %s - %s" %
(extNumber, trueRspStart - trigger,
trueRspStop - trigger))
if (rspStop >= tstop):
#This matrix cover after the tstop,
#no reason to analize the other matrices
#pass
break
pass
pass
pass
pass
weight = []
if (len(rspList) > 1):
#We want to weight matrices by number of counts
#so we need the number of events falling in the current
#time interval
#Number of total events contained in the interval
nThisTotalEvt = nEvents
print("\n Total counts for this interval: %s" %
(nThisTotalEvt))
if (nThisTotalEvt <= 0):
print(
" Counts is zero: no signal here. Weight will be exposure-based."
)
#Weight according to the exposure
if (sum(weight) == 0):
for index, rsp in enumerate(rspList):
thisWeight = (rspStops[index] -
rspStarts[index]) / (tstop - tstart)
weight.append(thisWeight)
print(" Weight for response %s - %s: %s" %
(rspStarts[index] - trigger,
rspStops[index] - trigger, thisWeight))
pass
pass
else:
#Weight according to the counts
#now look upon the selected events, searching for events
#falling in the interval covered by each
#response matrix
for index, rsp in enumerate(rspList):
rspStart = rspStarts[index]
rspStop = rspStops[index]
#how many events of the time interval contained here?
nThisRspEvt = eventsCounter.getNevents(rspStart, rspStop)
#Compute the weight
if (nThisRspEvt > 0):
thisWeight = float(nThisRspEvt) / float(nThisTotalEvt)
else:
thisWeight = 0
pass
#Append the weight to the list of weights
weight.append(thisWeight)
#Print information
print(" Weight for response %s - %s: %s" %
(rspStart - trigger, rspStop - trigger, thisWeight))
print(" Counts in time covered by this rsp: %s" %
nThisRspEvt)
pass
pass
#Now, if the sum of the weight is not 1, redistribuite the lacking weight
#according to the exposure. This can happen due to precision problems
if (sum(weight) != 1):
lackingWeight = 1.0 - sum(weight)
for index, rsp in enumerate(rspList):
thisExposureWeight = (rspStops[index] -
rspStarts[index]) / (tstop - tstart)
weight[index] += (thisExposureWeight * lackingWeight)
pass
pass
print("\nTotal weight ---> %s" % (sum(weight)))
else:
#There is only one matrix...
weight = [1]
pass
print("\n")
#Use addrmf to add and weight the response matrix
#We don't use directly pyFits because dealing with response matrices
#can be dangerous due to the variable lenght column,
#and other features, so we leave the work to a specialized tool
#we need to create a file for every matrix, and an ascii file
#containing the list of those files and the weights to be applied
asciiFilename = "__rspweight_rspList.ascii"
asciiFile = open(asciiFilename, 'w')
asciiFile.write('')
#Loop on the matrices and sum them
names = []
for rsp_i in range(len(rspList)):
#Decide a temporary filename
name = "__rspweight_matrix" + str(rsp_i) + ".rsp"
names.append(name)
primaryHDU = rsp2File[0].copy()
eboundsHDU = rsp2File['EBOUNDS'].copy()
responseHDU = rspList[rsp_i].copy()
HDUlist = pyfits.HDUList([primaryHDU, eboundsHDU, responseHDU])
HDUlist.writeto(name, output_verify='fix', clobber=True)
if (instrument.find("GBM") >= 0):
# FIX the wrong GBM matrices
data = pyfits.open(name, 'update')
#EBOUNDS:
#Find what is the tlminColumn column
columns = data['EBOUNDS'].data.names
tlminID = columns.index('CHANNEL') + 1
#Find the number of channels (usually 128)
nChannels = data['EBOUNDS'].data.size
#Fix the CHANNEL column, and TLMIN/TLMAX keywords
data['EBOUNDS'].data.field('CHANNEL')[:] = numpy.array(
range(1, nChannels + 1))
data['EBOUNDS'].header.set("TLMIN%s" % (tlminID), 1)
data['EBOUNDS'].header.set("TLMAX%s" % (tlminID), nChannels)
#SPECRESP MATRIX
#Now we have to correct the rows whith wrong F_CHAN and N_CHAN values:
#There are rows where F_CHAN = 128, and N_CHAN = 1, but the only element not null
#in the row is the first, thus it should be: F_CHAN = 1, N_CHAN = 1
for row in range(nChannels):
if (data['SPECRESP MATRIX'].data.field('F_CHAN')[row]
== 128 and
data['SPECRESP MATRIX'].data.field('N_CHAN')[row]
== 1):
#We have a possibly wrong row
#Try to access the channel 128 of the matrix
try:
data['SPECRESP MATRIX'].data.field(
'MATRIX')[row][127]
except:
#Impossible to access it, thus the values are wrong. Fix them
data['SPECRESP MATRIX'].data.field(
'F_CHAN')[row] = numpy.array([1])
data['SPECRESP MATRIX'].data.field(
'N_CHAN')[row] = numpy.array([1])
pass
pass
pass
#Find what is the tlminColumn column
columns = data['SPECRESP MATRIX'].data.names
tlminID = columns.index('F_CHAN') + 1
data['SPECRESP MATRIX'].header.set("TLMIN%s" % (tlminID), 1)
data['SPECRESP MATRIX'].header.set("TLMAX%s" % (tlminID),
nChannels)
data.close()
pass
asciiFile.write(name + " " + str(weight[rsp_i]) + "\n")
pass
asciiFile.close()
#Decide a name for the output matrix for this interval
thisRspName = "__rspweight_interval" + str(intervalNumber) + ".rsp"
createdRspNames.append(thisRspName)
if (len(names) == 1):
#Only one matrix. No need to weight
shutil.copy(names[0], thisRspName)
else:
#Run addrmf and weight the relevant matrices
cmdline = "addrmf @%s rmffile=%s clobber=yes" % (asciiFilename,
thisRspName)
print("\n %s \n" % (cmdline))
out, err = _callSubprocess(cmdline)
print out
print err
pass
#clean up
os.remove(asciiFilename)
pass
#now append all the produced matrix in one RSP2 file
#Take the PRIMARY extension from the input RSP2 file
primary = rsp2File[0].copy()
primary.header.set("DRM_NUM", nIntervals)
primary.header.set("TSTART", timeIntervals.tstarts[0])
primary.header.set("TSTOP", timeIntervals.tstops[-1])
primary.writeto(outrsp, clobber='True')
#Get the EBOUNDS extension from the first matrix
firstRsp = pyfits.open(createdRspNames[0])
ebounds = firstRsp['EBOUNDS'].copy()
firstRsp.close()
#Reopen the file just created
outRsp2 = pyfits.open(outrsp, 'update')
outRsp2.append(ebounds)
#now write an extension for each interval
for i in range(nIntervals):
thisRsp = pyfits.open(createdRspNames[i])
try:
curM = fixMatrixHDU(thisRsp["SPECRESP MATRIX"]).copy()
name = "SPECRESP MATRIX"
except:
try:
curM = fixMatrixHDU(thisRsp["SPECRESP MATRIX"]).copy()
name = "MATRIX"
except:
raise RuntimeError(
"Something gone wrong. Invalid file produced by the weighting algorithm."
)
pass
thisRsp.close()
curHeader = curM.header
curData = curM.data
#Update TSTART and TSTOP
curHeader.set("TSTART", timeIntervals.tstarts[intervalNumber])
curHeader.set("TSTOP", timeIntervals.tstops[intervalNumber])
#Update RSP_NUM keyword
curHeader.set("RSP_NUM", i + 1)
#update EXTVER keyword
curHeader.set("EXTVER", i + 1)
#Add history
history = "This is a matrix computed by weighting applying matrices contained in " + rsp2
curHeader.add_history(history)
print("Appending matrix number %s..." % (i + 1))
#Write this as an extension in the output file
outRsp2.append(curM)
pass
#Closing everything
outRsp2.close()
#Close files
rsp2File.close()
#Cleanup
print("Cleaning up...")
fileToDelete = glob.glob("__rspweight*")
for f in fileToDelete:
os.remove(f)
def RSPweight(**kwargs):
'''
Weight the response matrices contained in the rsp file.
Usage:
RSPweight(eventfile=eventfile,timeBinsFile=timeBinsFile,rsp2file=rsp2file,
outfile=outfile,triggerTime=triggerTime)
where eventfile can be either a TTE/FT1 file or a CSPEC file.
'''
timeIntervals = TimeIntervals(**kwargs)
rsp2 = _fixPath(kwargs['rsp2file'])
out = _fixPath(kwargs['outfile'])
#The trigger time is only for printing purposes: if specified,
#all the messages from the program will contain time intervals referred
#to the trigger time, otherwise they will be in MET
if('triggerTime' in kwargs.keys()):
trigger = kwargs['triggerTime']
else:
trigger = 0
#name for the output
outrsp = out
#Open files
rsp2File = pyfits.open(rsp2)
#Get instrument name
try:
instrument = pyfits.getval(_fixPath(kwargs['eventfile']),"INSTRUME",extname="EVENTS",extver=1)
except:
try:
instrument = pyfits.getval(_fixPath(kwargs['eventfile']),"INSTRUME",extname="SPECTRUM",extver=1)
except:
instrument = "UNKN-INSTRUME"
#For every interval contained in the time bins file
#find the applying matrices and compute the weight (if necessary)
nIntervals = len(timeIntervals.tstarts)
nMatrix = len(rsp2File)
createdRspNames = []
eventsCounter = EventsCounter(**kwargs)
for tstart,tstop,nEvents,intervalNumber in zip(timeIntervals.tstarts,
timeIntervals.tstops,
timeIntervals.counts,
range(nIntervals)):
print("\nInterval: %s - %s" % (tstart-trigger,tstop-trigger))
#find the RSP matrices falling in the current tstart-tstop interval
rspList = []
rspStarts = []
rspStops = []
firstResponse = True
for extNumber in range(nMatrix):
if(rsp2File[extNumber].name=="SPECRESP MATRIX" or
rsp2File[extNumber].name=="MATRIX"):
curHeader = rsp2File[extNumber].header
#Check that the instrument is the same of the TTE file
instrument2 = curHeader["INSTRUME"]
if(instrument2!=instrument):
print("WARNING: the Events file %s and the response matrix file %s refers to different instruments" % (instrument,instrument2))
pass
#Find the start and stop time of the interval covered
#by this response matrix
headerStart = curHeader["TSTART"]
headerStop = curHeader["TSTOP"]
#The matrix cover the period going from the middle point between its start
#time and the start time of the previous matrix (or its start time if it is the
#first matrix of the file), and the middle point between its start time and its
#stop time (that is equal to the start time of the next one):
#
# tstart tstop
# |==================================|
# |--------------x---------------|-----------x-----------|----------x--..
#rspStart1 rspStop1= headerStart2 rspStop2= headerStart3 rspStop3
# rspStart2 rspStart3
#
#covered by: |m1 | m2 | m3|
#
if(firstResponse):
#This is the first matrix
if(extNumber==len(rsp2File)-1):
#This is both the first both the last matrix
rspStart = headerStart
rspStop = headerStop
else:
rspStart = headerStart
rspStop = (headerStart+headerStop)/2.0
firstResponse = False
pass
elif(extNumber==len(rsp2File)-1):
#This is the last matrix
rspStart = prevRspStop
rspStop = headerStop
else:
rspStart = prevRspStop
rspStop = (headerStart+headerStop)/2.0
pass
#Save the stop time for the next iteration of the loop
prevRspStop = rspStop
#Check if the current matrix covers at least a portion of the
#start-stop time interval
if( rspStop >= tstart and rspStart <= tstop):
#Found a matrix covering a part of the interval:
#adding it to the list:
rspList.append(fixMatrixHDU(rsp2File[extNumber]))
#Get the "true" start time of the time sub-interval covered by this matrix
trueRspStart = max(rspStart,tstart)
#Get the "true" stop time of the time sub-interval covered by this matrix
if(extNumber==len(rsp2File)-1):
#Since there are no matrices after this one, this has to cover until the end of the interval
if(tstop > rspStop):
print("\nWARNING: RSPweight: The RSP file does not cover the required time interval.")
print(" The last response should cover from %s to %s, but we'll use it" % (trueRspStart, rspStop))
print(" to cover until the end of the time interval (%s)." % (tstop))
trueRspStop = tstop
else:
trueRspStop = tstop
else:
trueRspStop = min(rspStop,tstop)
pass
#Save the true start and stop
rspStarts.append(trueRspStart)
rspStops.append(trueRspStop)
print(" Matr. in ext #%s covers %s - %s" % (extNumber,trueRspStart-trigger,trueRspStop-trigger))
if(rspStop >= tstop):
#This matrix cover after the tstop,
#no reason to analize the other matrices
#pass
break
pass
pass
pass
pass
weight = []
if(len(rspList) > 1):
#We want to weight matrices by number of counts
#so we need the number of events falling in the current
#time interval
#Number of total events contained in the interval
nThisTotalEvt = nEvents
print("\n Total counts for this interval: %s" %(nThisTotalEvt))
if(nThisTotalEvt <= 0):
print(" Counts is zero: no signal here. Weight will be exposure-based.")
#Weight according to the exposure
if(sum(weight)==0):
for index,rsp in enumerate(rspList):
thisWeight = (rspStops[index]-rspStarts[index])/(tstop-tstart)
weight.append(thisWeight)
print(" Weight for response %s - %s: %s" % (rspStarts[index]-trigger,rspStops[index]-trigger,thisWeight))
pass
pass
else:
#Weight according to the counts
#now look upon the selected events, searching for events
#falling in the interval covered by each
#response matrix
for index,rsp in enumerate(rspList):
rspStart = rspStarts[index]
rspStop = rspStops[index]
#how many events of the time interval contained here?
nThisRspEvt = eventsCounter.getNevents(rspStart,rspStop)
#Compute the weight
if(nThisRspEvt > 0):
thisWeight = float(nThisRspEvt)/float(nThisTotalEvt)
else:
thisWeight = 0
pass
#Append the weight to the list of weights
weight.append(thisWeight)
#Print information
print(" Weight for response %s - %s: %s" % (rspStart-trigger,rspStop-trigger,thisWeight))
print(" Counts in time covered by this rsp: %s" % nThisRspEvt)
pass
pass
#Now, if the sum of the weight is not 1, redistribuite the lacking weight
#according to the exposure. This can happen due to precision problems
if(sum(weight)!=1):
lackingWeight = 1.0-sum(weight)
for index,rsp in enumerate(rspList):
thisExposureWeight = (rspStops[index]-rspStarts[index])/(tstop-tstart)
weight[index] += (thisExposureWeight*lackingWeight)
pass
pass
print("\nTotal weight ---> %s" %(sum(weight)))
else:
#There is only one matrix...
weight = [1]
pass
print("\n")
#Use addrmf to add and weight the response matrix
#We don't use directly pyFits because dealing with response matrices
#can be dangerous due to the variable lenght column,
#and other features, so we leave the work to a specialized tool
#we need to create a file for every matrix, and an ascii file
#containing the list of those files and the weights to be applied
asciiFilename = "__rspweight_rspList.ascii"
asciiFile = open(asciiFilename,'w')
asciiFile.write('')
#Loop on the matrices and sum them
names = []
for rsp_i in range(len(rspList)):
#Decide a temporary filename
name = "__rspweight_matrix"+str(rsp_i)+".rsp"
names.append(name)
primaryHDU = rsp2File[0].copy()
eboundsHDU = rsp2File['EBOUNDS'].copy()
responseHDU = rspList[rsp_i].copy()
HDUlist = pyfits.HDUList([primaryHDU,eboundsHDU,responseHDU])
HDUlist.writeto(name,output_verify='fix',clobber=True)
if(instrument.find("GBM")>=0):
# FIX the wrong GBM matrices
data = pyfits.open(name,'update')
#EBOUNDS:
#Find what is the tlminColumn column
columns = data['EBOUNDS'].data.names
tlminID = columns.index('CHANNEL')+1
#Find the number of channels (usually 128)
nChannels = data['EBOUNDS'].data.size
#Fix the CHANNEL column, and TLMIN/TLMAX keywords
data['EBOUNDS'].data.field('CHANNEL')[:]=numpy.array(range(1,nChannels+1))
data['EBOUNDS'].header.set("TLMIN%s" %(tlminID),1)
data['EBOUNDS'].header.set("TLMAX%s" %(tlminID),nChannels)
#SPECRESP MATRIX
#Now we have to correct the rows whith wrong F_CHAN and N_CHAN values:
#There are rows where F_CHAN = 128, and N_CHAN = 1, but the only element not null
#in the row is the first, thus it should be: F_CHAN = 1, N_CHAN = 1
for row in range(nChannels):
if(data['SPECRESP MATRIX'].data.field('F_CHAN')[row]==128 and
data['SPECRESP MATRIX'].data.field('N_CHAN')[row]==1):
#We have a possibly wrong row
#Try to access the channel 128 of the matrix
try:
data['SPECRESP MATRIX'].data.field('MATRIX')[row][127]
except:
#Impossible to access it, thus the values are wrong. Fix them
data['SPECRESP MATRIX'].data.field('F_CHAN')[row] = numpy.array([1])
data['SPECRESP MATRIX'].data.field('N_CHAN')[row] = numpy.array([1])
pass
pass
pass
#Find what is the tlminColumn column
columns = data['SPECRESP MATRIX'].data.names
tlminID = columns.index('F_CHAN')+1
data['SPECRESP MATRIX'].header.set("TLMIN%s" %(tlminID),1)
data['SPECRESP MATRIX'].header.set("TLMAX%s" %(tlminID),nChannels)
data.close()
pass
asciiFile.write(name+" "+str(weight[rsp_i])+"\n")
pass
asciiFile.close()
#Decide a name for the output matrix for this interval
thisRspName = "__rspweight_interval"+str(intervalNumber)+".rsp"
createdRspNames.append(thisRspName)
if(len(names)==1):
#Only one matrix. No need to weight
shutil.copy(names[0],thisRspName)
else:
#Run addrmf and weight the relevant matrices
cmdline = "addrmf @%s rmffile=%s clobber=yes" % (asciiFilename,thisRspName)
print("\n %s \n" %(cmdline))
out, err = _callSubprocess(cmdline)
print out
print err
pass
#clean up
os.remove(asciiFilename)
pass
#now append all the produced matrix in one RSP2 file
#Take the PRIMARY extension from the input RSP2 file
primary = rsp2File[0].copy()
primary.header.set("DRM_NUM",nIntervals)
primary.header.set("TSTART",timeIntervals.tstarts[0])
primary.header.set("TSTOP",timeIntervals.tstops[-1])
primary.writeto(outrsp,clobber='True')
#Get the EBOUNDS extension from the first matrix
firstRsp = pyfits.open(createdRspNames[0])
ebounds = firstRsp['EBOUNDS'].copy()
firstRsp.close()
#Reopen the file just created
outRsp2 = pyfits.open(outrsp,'update')
outRsp2.append(ebounds)
#now write an extension for each interval
for i in range(nIntervals):
thisRsp = pyfits.open(createdRspNames[i])
try:
curM = fixMatrixHDU(thisRsp["SPECRESP MATRIX"]).copy()
name = "SPECRESP MATRIX"
except:
try:
curM = fixMatrixHDU(thisRsp["SPECRESP MATRIX"]).copy()
name = "MATRIX"
except:
raise RuntimeError("Something gone wrong. Invalid file produced by the weighting algorithm.")
pass
thisRsp.close()
curHeader = curM.header
curData = curM.data
#Update TSTART and TSTOP
curHeader.set("TSTART",timeIntervals.tstarts[intervalNumber])
curHeader.set("TSTOP",timeIntervals.tstops[intervalNumber])
#Update RSP_NUM keyword
curHeader.set("RSP_NUM",i+1)
#update EXTVER keyword
curHeader.set("EXTVER",i+1)
#Add history
history="This is a matrix computed by weighting applying matrices contained in "+rsp2
curHeader.add_history(history)
print("Appending matrix number %s..." %(i+1))
#Write this as an extension in the output file
outRsp2.append(curM)
pass
#Closing everything
outRsp2.close()
#Close files
rsp2File.close()
#Cleanup
print("Cleaning up...")
fileToDelete = glob.glob("__rspweight*")
for f in fileToDelete:
os.remove(f)
def initWithTTE(self,tte):
f = pyfits.open(tte)
self.inputFile = 'TTE'
self.data = f["EVENTS",1].data.copy()
self.eventTimes = numpy.array(list(self.data.field('TIME')))
f.close()
def __init__(self, template_file):
self.proj = pyLike.SkyProj(template_file)
template = pyfits.open(template_file)
self.nx = template[0].header['NAXIS1']
self.ny = template[0].header['NAXIS2']
def gtltcube_mp(bins, SCFile, EVFile, OutFile, SaveTemp, zmax):
'''This functions looks at a spacecraft file and splits the time into
chunks that match the bin edges in the spacecraft file. It then
submits jobs based upon those start and stop times. This is to
make the resulting files as close to the original as possible.
Note that this assumes you are using the full time period in your
spacecraft file.'''
verbose = False
print "Opening event file to determine start and stop times..."
evfile = pyfits.open(EVFile, mode='readonly')
tstart = evfile[0].header['TSTART']
tstop = evfile[0].header['TSTOP']
gti_data = evfile[2].data
print "Opening SC file to determine break points..."
hdulist = pyfits.open(SCFile, mode='readonly')
scdata = hdulist[1].data
hdulist.close()
scstart = scdata.field('START')
scstop = scdata.field('STOP')
time_filter = (tstart <= scstart) & (scstop <= tstop)
redo = True
print "Checking for good times in the event file..."
while redo:
redo = False
scstartssplit = np.array_split(scstart[time_filter],int(bins))
scstopssplit = np.array_split(scstop[time_filter],bins)
#Explicitly set the first and last point to the values in the evfile header
scstartssplit[0][0] = tstart
scstopssplit[-1][-1] = tstop
starts = [st[0] for st in scstartssplit]
stops = [st[-1] for st in scstopssplit]
for interval in zip(starts,stops):
if verbose: print "Looking at interval",interval[0],"to",interval[1]
good_times = False
#grrrr. some bug in pyfits doesn't let me do this the python way...
for gti_i in range(len(gti_data)):
if(not good_times):
if verbose: print " Checking gti",gti_data[gti_i]['START'],"to",gti_data[gti_i]['STOP']
gti_starts = interval[0] <= gti_data[gti_i]['START'] <= interval[1]
gti_stops = interval[0] < gti_data[gti_i]['STOP'] <= interval[1]
if verbose: print " Does this gti start inside this interval? ", gti_starts
if verbose: print " Does this gti stop inside this interval? ", gti_stops
good_times = gti_starts or gti_stops
if verbose: print
if verbose: print " Are there good times inside this interval? ", good_times
if not good_times:
redo = True
if verbose: print
if redo:
if bins <= 1:
print "No good time intervals found. Bailing..."
sys.exit(1)
print "One (or more) of the slices doesn't have a GTI."
print "Reducing the number of threads from ",bins,"to",bins-1
bins -= 1
scfiles = [SCFile for st in scstartssplit]
evfiles = [EVFile for st in scstartssplit]
zmaxes = [zmax for st in scstartssplit]
pool = Pool(processes=bins)
times = np.array([starts,stops,scfiles,evfiles,zmaxes])
print "Spawning {} jobs...".format(bins)
tempfilenames = pool.map(ltcube,times.transpose())
print "Combining temporary files..."
ltsum(tempfilenames, OutFile, SaveTemp)
def run(**kwargs):
if(len(kwargs.keys())==0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
ra = thisCommand.getParValue('ra')
dec = thisCommand.getParValue('dec')
particlemodel = thisCommand.getParValue('particle_model')
galacticmodel = thisCommand.getParValue('galactic_model')
sourcemodel = thisCommand.getParValue('source_model')
filteredeventfile = thisCommand.getParValue('filteredeventfile')
xmlmodel = thisCommand.getParValue('xmlmodel')
triggername = thisCommand.getParValue('triggername')
ft2file = thisCommand.getParValue('ft2file')
fglmode = thisCommand.getParValue('fgl_mode')
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
#Get the IRF from the event file
try:
f = pyfits.open(filteredeventfile)
except:
raise GtBurstException(31,"Could not open filtered event file %s" %(filteredeventfile))
tstart = float(f[0].header['_TMIN'])
tstop = float(f[0].header['_TMAX'])
irf = str(f[0].header['_IRF'])
ra = float(f[0].header['_ROI_RA'])
dec = float(f[0].header['_ROI_DEC'])
roi = float(f[0].header['_ROI_RAD'])
#Lookup table for the models
models = {}
if(particlemodel=='isotr with pow spectrum'):
models['isotr with pow spectrum'] = LikelihoodComponent.IsotropicPowerlaw()
elif(particlemodel=='isotr template'):
models['isotr template'] = LikelihoodComponent.IsotropicTemplate(irf)
pass
if(galacticmodel=='template'):
models['template'] = LikelihoodComponent.GalaxyAndExtragalacticDiffuse(irf,ra,dec,2.5*roi)
elif(galacticmodel=='template (fixed norm.)'):
models['template (fixed norm.)'] = LikelihoodComponent.GalaxyAndExtragalacticDiffuse(irf,ra,dec,2.5*roi)
models['template (fixed norm.)'].fixNormalization()
pass
deltat = numpy.sum(f['GTI'].data.field('STOP')-f['GTI'].data.field('START'))
f.close()
triggertime = dataHandling.getTriggerTime(filteredeventfile)
if(irf.lower().find('source')>=0 and particlemodel!='isotr template'):
raise GtBurstException(6,"Do not use '%s' as model for the particle background in SOURCE class. Use '%s' instead."
%(particlemodel,'isotropic template'))
modelsToUse = [LikelihoodComponent.PointSource(ra,dec,triggername,sourcemodel)]
if(particlemodel!='none'):
if(particlemodel=='bkge'):
if(ft2file is None or ft2file==''):
raise ValueError("If you want to use the BKGE, you have to provide an FT2 file!")
modelsToUse.append(LikelihoodComponent.BKGETemplate(filteredeventfile,
ft2file,tstart,tstop,triggername,triggertime))
else:
modelsToUse.append(models[particlemodel])
if(galacticmodel!='none'):
modelsToUse.append(models[galacticmodel])
xml = LikelihoodComponent.LikelihoodModel()
xml.addSources(*modelsToUse)
xml.writeXML(xmlmodel)
if(fglmode=='complete'):
#Use a very large delta T so that
#all FGL sources are included in the
#xml model
deltat = 1e12
xml.add2FGLsources(ra,dec,float(roi)+8.0,xmlmodel,deltat)
dataHandling._writeParamIntoXML(xmlmodel,IRF=irf,OBJECT=triggername,RA=ra,DEC=dec)
return 'xmlmodel', xmlmodel
def __init__(self,ft1file,skyimage,figure,obj_ra=None,obj_dec=None):
self.skyimage = skyimage
ft1 = pyfits.open(ft1file)
self.events = ft1['EVENTS'].data
self.empty = False
if(len(self.events)==0):
print("No events in FT1 file %s" %(ft1file))
self.empty = True
#raise RuntimeError("No events in FT1 file %s" %(ft1file))
pass
self.obj_ra = obj_ra
self.obj_dec = obj_dec
#Read in the different classes of events
self.trigtime = dataHandling.getTriggerTime(ft1file)
time = self.events.field("TIME")
energy = self.events.field("ENERGY")
if(not self.empty):
self.tmin = min(time)-self.trigtime
self.tmax = max(time)-self.trigtime
self.energyMin = min(energy)
self.energyMax = max(energy)
else:
self.tmin = float(ft1['EVENTS'].header['TSTART'])
self.tmax = float(ft1['EVENTS'].header['TSTOP'])
self.energyMin = 100
self.energyMax = 1e7
pass
#Get the reprocessing
self.reprocVer = str(ft1[0].header['PROC_VER'])
self.generateColorMap()
#Print a summary
irfs = numpy.array(map(lambda x:IRFS.fromEvclassToIRF(self.reprocVer,x),self.events.field("EVENT_CLASS")))
print("")
for irf in IRFS.PROCS[self.reprocVer]:
try:
n = irfs[irfs==irf].shape[0]
except:
n = 0
pass
print("%-50s %s" %("Class %s only:" % irf,n))
self.pickerID = None
self.oldxmin = -1e9
self.oldxmax = 1e9
self.oldymin = -1e9
self.oldymax = 1e9
self.user_ra = None
self.user_dec = None
self.evtext = None
self.figure = figure
self.figure.clear()
self.displayImage()
self.initEventDisplay()
self.displayEvents()
self.figure.canvas.draw()
self.connectEvents()
ft1.close()
def __init__(self, ft1file, skyimage, figure, obj_ra=None, obj_dec=None):
self.skyimage = skyimage
ft1 = pyfits.open(ft1file)
self.events = ft1['EVENTS'].data
self.empty = False
if (len(self.events) == 0):
print("No events in FT1 file %s" % (ft1file))
self.empty = True
#raise RuntimeError("No events in FT1 file %s" %(ft1file))
pass
self.obj_ra = obj_ra
self.obj_dec = obj_dec
#Read in the different classes of events
self.trigtime = dataHandling.getTriggerTime(ft1file)
time = self.events.field("TIME")
energy = self.events.field("ENERGY")
if (not self.empty):
self.tmin = min(time) - self.trigtime
self.tmax = max(time) - self.trigtime
self.energyMin = min(energy)
self.energyMax = max(energy)
else:
self.tmin = float(ft1['EVENTS'].header['TSTART'])
self.tmax = float(ft1['EVENTS'].header['TSTOP'])
self.energyMin = 100
self.energyMax = 1e7
pass
#Get the reprocessing
self.reprocVer = str(ft1[0].header['PROC_VER'])
self.generateColorMap()
#Print a summary
irfs = numpy.array(
map(lambda x: IRFS.fromEvclassToIRF(self.reprocVer, x),
self.events.field("EVENT_CLASS")))
print("")
for irf in IRFS.PROCS[self.reprocVer]:
try:
n = irfs[irfs == irf].shape[0]
except:
n = 0
pass
print("%-50s %s" % ("Class %s only:" % irf, n))
self.pickerID = None
self.oldxmin = -1e9
self.oldxmax = 1e9
self.oldymin = -1e9
self.oldymax = 1e9
self.user_ra = None
self.user_dec = None
self.evtext = None
self.figure = figure
self.figure.clear()
self.displayImage()
self.initEventDisplay()
self.displayEvents()
self.figure.canvas.draw()
self.connectEvents()
ft1.close()
def run(**kwargs):
if(len(kwargs.keys())==0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('eventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
ra = thisCommand.getParValue('ra')
dec = thisCommand.getParValue('dec')
rad = thisCommand.getParValue('rad')
irf = thisCommand.getParValue('irf')
zmax = thisCommand.getParValue('zmax')
tstart = thisCommand.getParValue('tstart')
tstop = thisCommand.getParValue('tstop')
emin = thisCommand.getParValue('emin')
emax = thisCommand.getParValue('emax')
skybinsize = thisCommand.getParValue('skybinsize')
outfile = thisCommand.getParValue('skymap')
strategy = thisCommand.getParValue('strategy')
thetamax = float(thisCommand.getParValue('thetamax'))
allowEmpty = _yesOrNoToBool(thisCommand.getParValue('allowEmpty'))
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
from GtBurst import dataHandling
global lastDisplay
LATdata = dataHandling.LATData(eventfile,rspfile,ft2file)
if(strategy.lower()=="time"):
#gtmktime cut
filteredFile,nEvents = LATdata.performStandardCut(ra,dec,rad,irf,tstart,tstop,emin,emax,zmax,thetamax,True,strategy='time')
elif(strategy.lower()=="events"):
#no gtmktime cut, Zenith cut applied directly to the events
filteredFile,nEvents = LATdata.performStandardCut(ra,dec,rad,irf,tstart,tstop,emin,emax,zmax,thetamax,True,strategy='events')
pass
LATdata.doSkyMap(outfile,skybinsize)
#Now open the output file and get exposure and total number of events
skymap = pyfits.open(outfile)
totalNumberOfEvents = numpy.sum(skymap[0].data)
totalTime = numpy.sum(skymap['GTI'].data.field('STOP')-skymap['GTI'].data.field('START'))
skymap.close()
print("\nTotal number of events in the counts map: %s" %(totalNumberOfEvents))
print("Total time in Good Time Intervals: %s" %(totalTime))
if((totalTime==0) and allowEmpty==False):
raise GtBurstException(2,"Your filter resulted in zero exposure. \n\n" +
" Loose your cuts, or enlarge the time interval. You might want to "+
" check also the navigation plots (in the Tools menu) to make sure "+
" that the ROI is within the LAT FOV in the desired time interval.")
displ = None
if(figure is not None):
if(lastDisplay is not None):
lastDisplay.unbind()
pass
from GtBurst.InteractiveFt1Display import InteractiveFt1Display
lastDisplay = InteractiveFt1Display(filteredFile,outfile,figure,ra,dec)
pass
return 'skymap', outfile, 'filteredeventfile', filteredFile, 'irf', irf, 'eventDisplay', lastDisplay
def run(**kwargs):
if (len(kwargs.keys()) == 0):
#Nothing specified, the user needs just help!
thisCommand.getHelp()
return
pass
#Get parameters values
thisCommand.setParValuesFromDictionary(kwargs)
try:
eventfile = thisCommand.getParValue('eventfile')
rspfile = thisCommand.getParValue('rspfile')
ft2file = thisCommand.getParValue('ft2file')
ra = thisCommand.getParValue('ra')
dec = thisCommand.getParValue('dec')
rad = thisCommand.getParValue('rad')
irf = thisCommand.getParValue('irf')
zmax = thisCommand.getParValue('zmax')
tstart = thisCommand.getParValue('tstart')
tstop = thisCommand.getParValue('tstop')
emin = thisCommand.getParValue('emin')
emax = thisCommand.getParValue('emax')
skybinsize = thisCommand.getParValue('skybinsize')
outfile = thisCommand.getParValue('skymap')
strategy = thisCommand.getParValue('strategy')
thetamax = float(thisCommand.getParValue('thetamax'))
allowEmpty = _yesOrNoToBool(thisCommand.getParValue('allowEmpty'))
clobber = _yesOrNoToBool(thisCommand.getParValue('clobber'))
verbose = _yesOrNoToBool(thisCommand.getParValue('verbose'))
figure = thisCommand.getParValue('figure')
except KeyError as err:
print("\n\nERROR: Parameter %s not found or incorrect! \n\n" %
(err.args[0]))
#Print help
print thisCommand.getHelp()
return
pass
from GtBurst import dataHandling
global lastDisplay
LATdata = dataHandling.LATData(eventfile, rspfile, ft2file)
if (strategy.lower() == "time"):
#gtmktime cut
filteredFile, nEvents = LATdata.performStandardCut(ra,
dec,
rad,
irf,
tstart,
tstop,
emin,
emax,
zmax,
thetamax,
True,
strategy='time')
elif (strategy.lower() == "events"):
#no gtmktime cut, Zenith cut applied directly to the events
filteredFile, nEvents = LATdata.performStandardCut(ra,
dec,
rad,
irf,
tstart,
tstop,
emin,
emax,
zmax,
thetamax,
True,
strategy='events')
pass
LATdata.doSkyMap(outfile, skybinsize)
#Now open the output file and get exposure and total number of events
skymap = pyfits.open(outfile)
totalNumberOfEvents = numpy.sum(skymap[0].data)
totalTime = numpy.sum(skymap['GTI'].data.field('STOP') -
skymap['GTI'].data.field('START'))
skymap.close()
print("\nTotal number of events in the counts map: %s" %
(totalNumberOfEvents))
print("Total time in Good Time Intervals: %s" % (totalTime))
if ((totalTime == 0) and allowEmpty == False):
raise GtBurstException(
2, "Your filter resulted in zero exposure. \n\n" +
" Loose your cuts, or enlarge the time interval. You might want to "
+
" check also the navigation plots (in the Tools menu) to make sure "
+
" that the ROI is within the LAT FOV in the desired time interval."
)
displ = None
if (figure is not None):
if (lastDisplay is not None):
lastDisplay.unbind()
pass
from GtBurst.InteractiveFt1Display import InteractiveFt1Display
lastDisplay = InteractiveFt1Display(filteredFile, outfile, figure, ra,
dec)
pass
return 'skymap', outfile, 'filteredeventfile', filteredFile, 'irf', irf, 'eventDisplay', lastDisplay
required=True)
parser.add_argument("--outfile",
help="File for the results (will be overwritten)",
type=str,
required=True)
#Main code
if __name__ == "__main__":
args = parser.parse_args()
if (not os.path.exists(args.infile)):
raise RuntimeError("File %s does not exist" % (args.infile))
#Read input file
with pyfits.open(args.infile) as f:
data = f['EVENTS'].data
tstart = f['EVENTS'].header.get("TSTART")
tstop = f['EVENTS'].header.get("TSTOP")
time = data.field("TIME")
#This is probably useless, but sometimes input files
#are not time ordered and the BB algorithm would
#crash
time.sort()
BayesianBlocks.logger.setLevel(logging.DEBUG)
def __init__(self, template_file):
self.proj = pyLike.SkyProj(template_file)
template = pyfits.open(template_file)
self.nx = template[0].header['NAXIS1']
self.ny = template[0].header['NAXIS2']